Organizations: Faculty of Data Science, City University of Macau, 999078, SAR Macao, China · College of Electronic and Information Engineering, Shanghai Research Institute for Autonomous Intelligent Systems, State Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, Shanghai, China · Department of Automation, Tsinghua University, Beijing, China · College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
Light-effect contamination poses a significant challenge to nighttime visibility enhancement. Most methods suppress light effects by estimating and decomposing them through prior-driven regularization, yet they are often limited by hand-crafted priors and ill-posed nature of decomposition. This work proposes Di2CycleSB, a unsupervised Cycle Schrödinger Bridge Transformer framework guided by dynamic integral image priors, for high-quality unsupervised nighttime visibility enhancement. Specifically, a novel light-effect estimator is introduced to parameterize Gaussian-like adaptive priors by aggregating dynamic integral image representations for non-uniform glow estimation. Then, we propose a prior-informed Generator that exploits light-effect representations to guide long-range dependency modeling within our specific Transformer blocks. We formulate light-effect suppression as a Schrödinger bridge problem and construct forward and backward bridges with cycle consistency constraints to achieve visually pleasing enhancement. Extensive experiments on real-world datasets demonstrate the remarkable effectiveness of our Di2CycleSB in enhancing nighttime visibility. In particular, it achieves effective end-to-end light-effect suppression without any regularization constraints and image decomposition. The code and models are available at https://github.com/LHTcode/Di2CycleSB.
Figures & tables
Fig. 1: Comparison of Di²CycleSB, UNIE [ 12 ] , NightEnhance [ 13 ] , and DehazeSB [ 14 ] on the Light-Effects [ 15 ] and NTIRE 2025 Challenge [ 16 ] datasets. Our method achieves the best performance across User Study , LMM-IQA , Q-Align [ 17 ] , CLIP-IQA+ [ 18 ] , MANIQA [ 19 ] , and LIQE [ 20 ] . For visualization, each metric is first normalized separately for each dataset and then averaged.
Fig. 2: Our results lead to a superior nighttime visibility experience. Di²CycleSB naturally suppresses light effects while preserving visual fidelity. All unsupervised and semi-supervised methods methods are retrained for a fair comparison. Please zoom in for better viewing.
Fig. 3: Overview of the Di²CycleSB framework. (a) illustrates the unpaired training process of Di²CycleSB. During training, we jointly optimize the forward and backward Schrödinger bridges at randomly sampled time steps and establish a cycle path to further regularize the transport process. (b) Di²LE estimates the light-effect map from the image intensity component. (c) The PI Generator performs light-effect suppression under the guidance of the estimated light-effect map. (d) The PIT Block is the core component of the PI Generator.
Fig. 4: Visual qualitative comparisons of state-of-the-art methods on the Light-Effects dataset.
Fig. 5: Visual qualitative comparisons of state-of-the-art methods on the NTIRE 2025 Challenge 2K resolution dataset.
Methods
Venue
User Study ↑
LMM-IQA ↑
Q-Align ↑
CLIP-IQA+ ↑
MANIQA ↑
LIQE ↑
(sampled/full dataset)
Original Dataset
–
–
–
3.368
0.603
0.368
2.745
EnlightenGAN [ 2 ]
TIP’21
-1.158
-1.635/-1.853
2.552
0.451
0.330
1.991
Dehazeformer [ 52 ]
TIP’23
-1.491
-1.011/-0.849
3.015
0.513
0.321
2.399
UNIE [ 12 ]
ECCV’22
0.356
-0.164/-1.991
3.070
0.480
0.274
1.839
NightEnhance [ 13 ]
MM’23
0.855
0.770 / 0.763
3.469
0.567
0.334
2.574
TABLE I: Quantitative comparison on the Light-Effects dataset via user studies and no-reference IQA metrics.
Methods
Venue
User Study ↑
LMM-IQA ↑
Q-Align ↑
CLIP-IQA+ ↑
MANIQA ↑
LIQE ↑
(sampled/full dataset)
Original Dataset
–
–
–
2.241
0.293
0.175
1.186
EnlightenGAN [ 2 ]
TIP’21
-0.678
-1.640/-1.700
1.603
0.301
0.156
1.164
Dehazeformer [ 52 ]
TIP’23
-0.416
-0.695/-0.667
1.960
0.306
0.153
1.154
UNIE [ 12 ]
ECCV’22
0.845
1.014 / 0.878
2.609
0.315
0.188
1.706
NightEnhance [ 13 ]
MM’23
0.206
0.222/0.093
1.942
0.325
0.152
1.168
TABLE II: Quantitative comparison on the NTIRE 2025 Challenge unpaired dataset via user studies and no-reference IQA metrics.
Experiments
GPT-5.6 Sol ↑
Q-Align ↑
CLIP-IQA+ ↑
MANIQA ↑
LIQE ↑
w/o Cycle
0.556
3.516
0.581
0.338
2.854
w/o SB
-1.157
3.391
0.561
0.342
2.719
w/o Di²LE
0.467
3.463
0.629
0.341
2.752
w/o PI generator
-1.601
2.722
0.478
0.190
1.373
w/o LLIE
0.681
3.547
0.634
0.348
2.899
Di²CycleSB
1.053
3.567
0.644
0.360
2.929
TABLE III: Breakdown ablation study of the proposed framework.
Fig. 6: Qualitative visual results of the ablation study. Please zoom in for better viewing.
Experiments
GPT-5.6 Sol ↑
Max-RGB input
-0.295
Gaussian kernel ( 5×5 )
-0.372
Gaussian kernel ( 11×11 )
0.795
Gaussian kernel ( 21×21 )
0.184
Gaussian kernel ( 31×31 )
-0.011
Gaussian kernel ( 43×43 )
-1.892
TABLE IV: Ablation study of the Di²LE module.
Fig. 7: Normalized heatmap visualizations of the K=5 dynamic integral regions (top row) and the corresponding dynamic weighting coefficients (bottom row). The top row shows that pixels closer to light-effect regions activate larger low-pass filtering windows, whereas pixels farther away from light effects tend to activate smaller ones.
Fig. 8: Visualization of the light-effect map estimated by the Di²LE module.
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Yasmin Yasin, Muhammad Usman, Ibrahim Radwan +1
King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia · Ontario Tech University, Oshawa, Canada · University of Western Australia, Perth, Australia +1
Poor lighting conditions significantly impact image quality, posing substantial challenges for image editing and visualization. Many existing enhancement methods aim at proposing complex models while neglecting the intrinsic information contained within low-light images. In this work, we propose the Self-Information Mining (SIMI) network, an innovative unsupervised framework that decomposes low-light images into multiple components based on bit-plane decomposition. Our approach allows mining intrinsic information without relying on external data. This not only accelerates model convergence but also improves performance and reduces computational overhead. The unsupervised nature of our method facilitates real-world applicability. Experiments conducted on standard benchmarks demonstrate that SIMI achieves state-of-the-art performance.
Xuanshuo Fu, Lei Kang, Javier Vazquez-Corral
Computer Vision Center, Barcelona, Spain · Computer Science Dept., Universitat Aut`onoma de Barcelona, Barcelona, Spain
Existing deep learning-based low-light enhancement methods are typically trained on limited datasets with single enhancement targets, which restricts their generalization ability and controllability in real-world applications. To overcome these limitations, we propose ControlLight, a controllable, consistent, and generalizable framework for low-light enhancement. We first construct a large-scale dataset of real-world degraded images with continuous illumination-strength supervision. To further ensure consistent outputs under different control strengths, we introduce a misalignment-aware weighted flow matching loss that preserves image structure across continuous enhancement strengths. ControlLight allows users to edit real-world degraded low-light images toward satisfactory enhancement results by flexibly controlling the strength while preserving visual consistency and realism. Extensive experiments show that ControlLight achieves state-of-the-art performance against existing low-light enhancement approaches while demonstrating strong continuous controllability and generalization to real-world scenarios.
Yufeng Yang, Jianzhuang Liu, Jisheng Chu +4
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences · Zhejiang University